Identification of potential biomarkers of esophageal squamous cell carcinoma using community detection algorithms

Potential biomarker genes are uncovered in this research by developing a unique methodology through the employment of six eminent community detection algorithms (CDAs) on four RNAseq esophageal squamous cell carcinoma (ESCC) datasets. RNAseq datasets are preprocessed using galaxy server followed by the identification of a subset of differentially expressed genes (DEGs). CDAs are applied separately on control and disease samples of DEGs to extract the hidden communities of the datasets. To identify the significant communities, ESCC elite genes are extracted from Genecards for subsequent downstream analysis towards the identification of potential biomarkers. Topological analysis is performed to support critical gene identification based on elite genes followed by a biological investigation. For biological investigation, gene enrichment and pathway analysis are implemented. Finally, a group of genes EPHB2, ABLIM3, ACER1, ABCD4, ARF6, ADRA1D, ATP6V1D, CLTB, ATP6V0A4, and AP1M1 are identified as ESCC possible biomarkers that carry both topological and biological significance.